library("tidyverse")
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library("Seurat")
Loading required package: cowplot
Attaching package: ‘cowplot’
The following object is masked from ‘package:ggplot2’:
ggsave
Loading required package: Matrix
Attaching package: ‘Matrix’
The following object is masked from ‘package:tidyr’:
expand
library("Matrix")
library("stringr")
library("knitr")
library("kableExtra")
library("pheatmap")
library("RColorBrewer")
library("clusterProfiler")
Loading required package: DOSE
DOSE v3.4.0 For help: https://guangchuangyu.github.io/DOSE
If you use DOSE in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis. Bioinformatics 2015, 31(4):608-609
clusterProfiler v3.4.4 For help: https://guangchuangyu.github.io/clusterProfiler
If you use clusterProfiler in published research, please cite:
Guangchuang Yu., Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.
Attaching package: ‘clusterProfiler’
The following object is masked from ‘package:purrr’:
simplify
library("ReactomePA")
ReactomePA v1.22.0 For help: https://guangchuangyu.github.io/ReactomePA
If you use ReactomePA in published research, please cite:
Guangchuang Yu, Qing-Yu He. ReactomePA: an R/Bioconductor package for reactome pathway analysis and visualization. Molecular BioSystems 2016, 12(2):477-479
save_csv <- TRUE
markers.biol.validated <- c("Thbs4","Cxcl14","Cd9","Nr2e1","Id2","Ascl1","Egfr","Dcx","Dlx1","Mki67","Sox2","S100b","Cd24a","Ift88","Foxj1","Pdgfrb","Pecam1","Slc1a3","Gfap","Nes","Cxcl10","Rpl32","Hes1","Hes5")
markers.custom.order <- c("Sfrp5","Bmpr1a","Vcam1","Slc1a3","Id2","Hes1","Hes5","Egfr","Ascl1","Mki67","Rpl22","Cd9","Nr2e1","Sox2","S100b")
cc.genes <- readLines(con = "cell_cycle_genes/cell_cycle_vignette_files/regev_lab_cell_cycle_genes.txt")
cc.genes <- str_to_title(cc.genes)
# Separate markers of G2/M phase and markers of S phase
s.genes <- cc.genes[1:43]
g2m.genes <- cc.genes[44:97]
datadir <- "count_table/filtered_gene_bc_matrices_mex/mm10/"
data_10X2 <- Read10X(data.dir = file.path( datadir) )
seurat_10X2 <- CreateSeuratObject(raw.data = data_10X2, min.cells = 3, min.genes = 1500, project = "young_vs_old_10X2")
mito.genes <- grep(pattern = "^mt-", x = rownames(x = seurat_10X2@data), value = TRUE)
percent.mito <- Matrix::colSums(seurat_10X2@raw.data[mito.genes, ])/Matrix::colSums(seurat_10X2@raw.data)
seurat_10X2 <- AddMetaData(object = seurat_10X2, metadata = percent.mito, col.name = "percent.mito")
VlnPlot(object = seurat_10X2, features.plot = c("nGene", "nUMI", "percent.mito"), nCol = 3 )
We can also annotate the age of the mouse from which the cells were prepared (young or old). We know that all cell barcodes ending in ...-1 are beloning to cells from old old mice, the cell barcodes ending in ...-2 are from young mice.
age.cells <- data.frame( age = as.factor(rownames(seurat_10X2@meta.data)) )
rownames(age.cells) <- rownames(seurat_10X2@meta.data)
age.cells$age <- stringr::str_replace(age.cells$age , pattern = "^\\w+-1$" , replacement = "old")
age.cells$age <- stringr::str_replace(age.cells$age , pattern = "^\\w+-2$" , replacement = "young")
age.cells$age_num <- age.cells$age
age.cells$age_num <- stringr::str_replace(age.cells$age_num , pattern = "old" , replacement = "1")
age.cells$age_num <- stringr::str_replace(age.cells$age_num , pattern = "young" , replacement = "2")
age.cells$age_num <- as.numeric(age.cells$age_num)
age.cells$age <- factor(age.cells$age)
seurat_10X2 <- AddMetaData(seurat_10X2, age.cells , "age")
par(mfrow = c(1, 2))
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "percent.mito")
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "nGene")
seurat_10X2 <- FilterCells(object = seurat_10X2, subset.names = c("nGene", "percent.mito"),
low.thresholds = c(1500, -Inf), high.thresholds = c(4500, 0.10))
par(mfrow = c(1, 2))
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "percent.mito")
GenePlot(object = seurat_10X2, gene1 = "nUMI", gene2 = "nGene")
seurat_10X2 <- NormalizeData(object = seurat_10X2, normalization.method = "LogNormalize", scale.factor = 10000)
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
seurat_10X2 <- ScaleData(object = seurat_10X2, vars.to.regress = c("nUMI", "percent.mito" , "nGene"))
seurat_10X2 <- CellCycleScoring(object = seurat_10X2, s.genes = s.genes, g2m.genes = g2m.genes, set.ident = TRUE)
We can see that the columns S.Score, G2M.Score and Phase are now added to the meta data table
head(seurat_10X2@meta.data)
seurat_10X2@meta.data$CC.Difference <- seurat_10X2@meta.data$S.Score - seurat_10X2@meta.data$G2M.Score
seurat_10X2 <- ScaleData(object = seurat_10X2, vars.to.regress = "CC.Difference", display.progress = FALSE)
seurat_10X2 <- FindVariableGenes(object = seurat_10X2, mean.function = ExpMean, dispersion.function = LogVMR,
x.low.cutoff = 0.0125, x.high.cutoff = 4, y.cutoff = 0.5)
Calculating gene means
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|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
|----|----|----|----|----|----|----|----|----|----|
**************************************************|
Using these parameters we have identified 2236 genes as variable.
Now we use the detected variable genes to peform PCA on the data
seurat_10X2 <- RunPCA(object = seurat_10X2, pc.genes = seurat_10X2@var.genes, do.print = TRUE, pcs.print = 1:5, genes.print = 5 , seed.use = 2 )
seurat_10X2 <- ProjectPCA(object = seurat_10X2 )
seurat_10X2 <- JackStraw(object = seurat_10X2 )
PCElbowPlot(object = seurat_10X2)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 5)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 5)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 5)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 5)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 4)
seurat_10X2 <- FindClusters(seurat_10X2, reduction.type = "pca", dims.use = 1:8, save.SNN = T , force.recalc = TRUE)
TSNEPlot(object = seurat_10X2)
FeaturePlot(object = seurat_10X2 , features.plot = "age_num" , cols.use = c("red","forestgreen") , no.legend = FALSE )
FeaturePlot(object = seurat_10X2 , features.plot = "nUMI" , cols.use = c("lightgrey","red") , no.legend = FALSE )
FeaturePlot(object = seurat_10X2 , features.plot = "nGene" , cols.use = c("lightgrey","red") , no.legend = FALSE )
Let's look at the
markers.seurat_10X2 <- FindAllMarkers(seurat_10X2, print.bar = FALSE , only.pos = TRUE , return.thresh = 0.05)
markers.seurat_10X2 %>% filter(cluster == 0)
markers.seurat_10X2 %>% filter(cluster == 1)
markers.seurat_10X2 %>% filter(cluster == 2)
markers.seurat_10X2 %>% filter(cluster == 3)
markers.seurat_10X2 %>% filter(cluster == 4)
markers.seurat_10X2 %>% filter(cluster == 5)
markers.seurat_10X2 %>% filter(cluster == 6)
markers.seurat_10X2 %>% filter(cluster == 7)
markers.seurat_10X2 %>% filter(cluster == 8)
markers.seurat_10X2 %>% filter(cluster == 9)
markers.seurat_10X2 %>% filter(cluster == 10)
markers_cluster_6_vs_8 <- FindMarkers(object = seurat_10X2 , ident.1 = 6 , ident.2 = 8 , only.pos = TRUE , print.bar = FALSE )
markers_cluster_6_vs_8
Cluster 6 shows increased expression of Mapk-Pathway related genes, immediate-early genes and transcription factors, like: Jun, Fos, Atf3, Ier2 ...
markers_cluster_8_vs_6 <- FindMarkers(object = seurat_10X2 , ident.1 = 8 , ident.2 = 6 , only.pos = TRUE , print.bar = FALSE )
markers_cluster_8_vs_6
TSNEPlot(object = seurat_10X2)
## quiescent Markers
FeaturePlot(object = seurat_10X2 , features.plot = "Thbs4" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Id2" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Cxcl14" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Stem Cell markers
FeaturePlot(object = seurat_10X2 , features.plot = "Slc1a3" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Nr2e1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Hes1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Hes5" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Nes" ,cols.use = c("lightblue","red") , no.legend = FALSE)
##
FeaturePlot(object = seurat_10X2 , features.plot = "Cxcl10" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Proliferation markers
FeaturePlot(object = seurat_10X2 , features.plot = "Rpl22" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Rpl32" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Cd9" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Astrocyte markers
FeaturePlot(object = seurat_10X2 , features.plot = "S100b" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Gfap" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Active NSC markers
FeaturePlot(object = seurat_10X2 , features.plot = "Mki67" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Ascl1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Egfr" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## TAPs
FeaturePlot(object = seurat_10X2 , features.plot = "Vcam1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Dlx1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Neuroblast marker
FeaturePlot(object = seurat_10X2 , features.plot = "Dcx" ,cols.use = c("lightblue","red") , no.legend = FALSE)
##
FeaturePlot(object = seurat_10X2 , features.plot = "Sfrp5" ,cols.use = c("lightblue","red") , no.legend = FALSE)
## Oligodenrocyte marker
FeaturePlot(object = seurat_10X2 , features.plot = "Olig1" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Pdgfra" ,cols.use = c("lightblue","red") , no.legend = FALSE)
FeaturePlot(object = seurat_10X2 , features.plot = "Sox10" ,cols.use = c("lightblue","red") , no.legend = FALSE)
# Load the genes as vector
markers <- c( "Agt","Slc6a9","Etnppl","Slc6a1","Sparc",
"Slc1a3","Bcan","Tspan7","Htra1","Cldn10","Ptn","Acsl6","Fgfr3",
"Sparcl1","Atp1a2","Gpr37l1","Gja1","Prnp","Acsl3","Aqp4","Apoe","Gm26917", # markers for quiescent NSCs - Gm26917 = Rn45s
"Cst3","Clu","Slc1a2","Prdx6","Mt1","Aldoc", # shared between quiescent and primed NSCs - slc1a2 instead of scl1a2 (probably typo)
"Thbs4","Ntrk2","Fxyd1","Gstm1","Igfbp5","S100a6","Itm2b","Sfrp1","Dkk3","C4b",
"Acot1","Luc7l3","Ckb",
"Cpe","Dbi", # primed NSCs and early active NSCs
"Miat","Lima1","Pabpc1","Ascl1","Rpl12","Mycn","Olig2",
"Pcna","Hsp90aa1","Hnrnpab","Ran","Ppia",
"Eef1a1","Ptma","Rpl41","Npm1", "Rpsa", "Fabp7", "Egfr", # active NSCs
"Mki67","Dlx2","Dlx1","Cdca3","Dlx1as",
"Nrep","Tubb2b","Dcx","Btg1","Nfib",
"Gad1","Ndrg4","Snap25","Syt1","Rbfox3",
"Tmsb10","Stmn2","Cd24a","Dlx6os1","Tubb5","Tubb3","Ccnd2","Hmgn2","H2afz","Sox11","Tuba1b","Tmsb4x","Stmn1","Tpt1","Rpl18a"
)
interval <- c( seq( 1, length(markers) , by = round(length(markers)/4) ) , length(markers) )
interval
[1] 1 24 47 70 92
TSNEPlot(object = seurat_10X2)
for(i in seq( interval[1] , interval[2] )){
FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}
TSNEPlot(object = seurat_10X2)
for(i in seq( interval[2]+1 , interval[3] )){
FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}
TSNEPlot(object = seurat_10X2)
for(i in seq( interval[3]+1 , interval[4] )){
FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}
TSNEPlot(object = seurat_10X2)
for(i in seq( interval[4]+1 , interval[5] )){
FeaturePlot(object = seurat_10X2 , features.plot = markers[i] ,cols.use = c("lightblue","red") , no.legend = FALSE)
}
## quiescent Markers
VlnPlot(object = seurat_10X2 , features.plot = "Thbs4" )
VlnPlot(object = seurat_10X2 , features.plot = "Id2" )
## Stem Cell markers
VlnPlot(object = seurat_10X2 , features.plot = "Slc1a3" )
VlnPlot(object = seurat_10X2 , features.plot = "Nr2e1" )
VlnPlot(object = seurat_10X2 , features.plot = "Hes1" )
VlnPlot(object = seurat_10X2 , features.plot = "Hes5" )
VlnPlot(object = seurat_10X2 , features.plot = "Nes" )
##
VlnPlot(object = seurat_10X2 , features.plot = "Cxcl10" )
VlnPlot(object = seurat_10X2 , features.plot = "Cxcl14" )
## Proliferation markers
VlnPlot(object = seurat_10X2 , features.plot = "Rpl22" )
VlnPlot(object = seurat_10X2 , features.plot = "Rpl32" )
VlnPlot(object = seurat_10X2 , features.plot = "Cd9" )
## Astrocyte markers
VlnPlot(object = seurat_10X2 , features.plot = "S100b" )
VlnPlot(object = seurat_10X2 , features.plot = "Gfap" )
## Active NSC markers & TAPS
VlnPlot(object = seurat_10X2 , features.plot = "Mki67" )
VlnPlot(object = seurat_10X2 , features.plot = "Ascl1" )
VlnPlot(object = seurat_10X2 , features.plot = "Egfr" )
VlnPlot(object = seurat_10X2 , features.plot = "Vcam1" )
VlnPlot(object = seurat_10X2 , features.plot = "Dlx1" )
VlnPlot(object = seurat_10X2 , features.plot = "Dlx2" )
VlnPlot(object = seurat_10X2 , features.plot = "Atp1a2" )
## Neuroblast marker
VlnPlot(object = seurat_10X2 , features.plot = "Dcx" )
##
VlnPlot(object = seurat_10X2 , features.plot = "Sfrp5" )
## Oligodenrocyte marker
VlnPlot(object = seurat_10X2 , features.plot = "Olig1" )
VlnPlot(object = seurat_10X2 , features.plot = "Pdgfra" )
VlnPlot(object = seurat_10X2 , features.plot = "Sox10" )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(0) ) , ident.use = "qNSC1" )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(4) ) , ident.use = "qNSC2" )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(6,8) ) , ident.use = "aNSC0" )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(3,5) ) , ident.use = "aNSC1" )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(1) ) , ident.use = "aNSC2" )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(2) ) , ident.use = "TAP" )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(7) ) , ident.use = "NB" )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(10) ) , ident.use = "OPC" )
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = WhichCells(object = seurat_10X2 , ident = c(9) ) , ident.use = "OD" )
seurat_10X2 <- BuildClusterTree(object = seurat_10X2 , pcs.use = 1:8)
[1] "Finished averaging RNA for cluster aNSC0"
[1] "Finished averaging RNA for cluster aNSC1"
[1] "Finished averaging RNA for cluster aNSC2"
[1] "Finished averaging RNA for cluster NB"
[1] "Finished averaging RNA for cluster OD"
[1] "Finished averaging RNA for cluster OPC"
[1] "Finished averaging RNA for cluster qNSC1"
[1] "Finished averaging RNA for cluster qNSC2"
[1] "Finished averaging RNA for cluster TAP"
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 1, dim.2 = 5)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 2, dim.2 = 5)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 4)
PCAPlot(object = seurat_10X2 , dim.1 = 3, dim.2 = 5)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 4, dim.2 = 5)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 1)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 2)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 3)
PCAPlot(object = seurat_10X2 , dim.1 = 5, dim.2 = 4)
TSNEPlot(object = seurat_10X2)
TSNEPlot(object = seurat_10X2 , do.label = TRUE)
celltypes_order <- c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2","TAP","NB","OPC","OD")
## quiescent Markers
VlnPlot(object = seurat_10X2 , features.plot = "Thbs4" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Id2" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Stem Cell markers
VlnPlot(object = seurat_10X2 , features.plot = "Slc1a3" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Nr2e1" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Hes1" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Hes5" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Nes" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
##
VlnPlot(object = seurat_10X2 , features.plot = "Cxcl10" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Proliferation markers
VlnPlot(object = seurat_10X2 , features.plot = "Rpl22" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Rpl32" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Cd9" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Astrocyte markers
VlnPlot(object = seurat_10X2 , features.plot = "S100b" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Gfap" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Active NSC markers
VlnPlot(object = seurat_10X2 , features.plot = "Ascl1" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Egfr" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## TAPs
VlnPlot(object = seurat_10X2 , features.plot = "Vcam1" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Dlx1" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Neuroblast marker
VlnPlot(object = seurat_10X2 , features.plot = "Dcx" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
##
VlnPlot(object = seurat_10X2 , features.plot = "Sfrp5" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
## Oligodenrocyte marker
VlnPlot(object = seurat_10X2 , features.plot = "Olig1" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Pdgfra" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = "Sox10" , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = c("nUMI") , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = c("nGene") , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
VlnPlot(object = seurat_10X2 , features.plot = c("percent.mito") , do.return = TRUE) + scale_x_discrete( limits = celltypes_order )
markers_plots <- c( "Thbs4" , "Id2" , "Slc1a3" , "Nr2e1" , "Hes1" , "Hes5" , "Nes" , "Cxcl10" , "Rpl22" , "Rpl32" , "Cd9" , "S100b" , "Gfap" , "Mki67" , "Ascl1" , "Egfr" , "Vcam1" , "Dlx1" , "Dcx" , "Sfrp5" , "Olig1" , "Pdgfra" , "Sox10" )
DoHeatmap(object = seurat_10X2 , genes.use = markers_plots , slim.col.label = TRUE , col.low = "blue" , col.mid = "white" , col.high = "red" , group.label.rot = TRUE )
DoHeatmap(object = seurat_10X2 , genes.use = markers.custom.order , slim.col.label = TRUE , col.low = "blue" , col.mid = "white" , col.high = "red" , group.label.rot = TRUE )
DoHeatmap(object = seurat_10X2 , genes.use = markers , slim.col.label = TRUE , col.low = "blue" , col.mid = "white" , col.high = "red" , group.label.rot = TRUE )
seurat_10X2 <- StashIdent(object = seurat_10X2 , save.name = "celltype")
markers.seurat_10X2 <- FindAllMarkers(seurat_10X2)
|+ | 1 % ~26s
|++ | 2 % ~27s
|++ | 3 % ~27s
|+++ | 4 % ~27s
|+++ | 5 % ~26s
|++++ | 7 % ~26s
|++++ | 8 % ~26s
|+++++ | 9 % ~25s
|+++++ | 10% ~25s
|++++++ | 11% ~24s
|+++++++ | 12% ~24s
|+++++++ | 13% ~23s
|++++++++ | 14% ~23s
|++++++++ | 15% ~23s
|+++++++++ | 16% ~22s
|+++++++++ | 18% ~22s
|++++++++++ | 19% ~22s
|++++++++++ | 20% ~21s
|+++++++++++ | 21% ~21s
|+++++++++++ | 22% ~21s
|++++++++++++ | 23% ~20s
|+++++++++++++ | 24% ~20s
|+++++++++++++ | 25% ~20s
|++++++++++++++ | 26% ~19s
|++++++++++++++ | 27% ~19s
|+++++++++++++++ | 29% ~19s
|+++++++++++++++ | 30% ~18s
|++++++++++++++++ | 31% ~18s
|++++++++++++++++ | 32% ~18s
|+++++++++++++++++ | 33% ~18s
|++++++++++++++++++ | 34% ~17s
|++++++++++++++++++ | 35% ~17s
|+++++++++++++++++++ | 36% ~17s
|+++++++++++++++++++ | 37% ~17s
|++++++++++++++++++++ | 38% ~16s
|++++++++++++++++++++ | 40% ~16s
|+++++++++++++++++++++ | 41% ~16s
|+++++++++++++++++++++ | 42% ~15s
|++++++++++++++++++++++ | 43% ~15s
|++++++++++++++++++++++ | 44% ~15s
|+++++++++++++++++++++++ | 45% ~14s
|++++++++++++++++++++++++ | 46% ~14s
|++++++++++++++++++++++++ | 47% ~14s
|+++++++++++++++++++++++++ | 48% ~14s
|+++++++++++++++++++++++++ | 49% ~13s
|++++++++++++++++++++++++++ | 51% ~13s
|++++++++++++++++++++++++++ | 52% ~13s
|+++++++++++++++++++++++++++ | 53% ~12s
|+++++++++++++++++++++++++++ | 54% ~12s
|++++++++++++++++++++++++++++ | 55% ~12s
|+++++++++++++++++++++++++++++ | 56% ~11s
|+++++++++++++++++++++++++++++ | 57% ~11s
|++++++++++++++++++++++++++++++ | 58% ~11s
|++++++++++++++++++++++++++++++ | 59% ~11s
|+++++++++++++++++++++++++++++++ | 60% ~10s
|+++++++++++++++++++++++++++++++ | 62% ~10s
|++++++++++++++++++++++++++++++++ | 63% ~10s
|++++++++++++++++++++++++++++++++ | 64% ~09s
|+++++++++++++++++++++++++++++++++ | 65% ~09s
|+++++++++++++++++++++++++++++++++ | 66% ~09s
|++++++++++++++++++++++++++++++++++ | 67% ~09s
|+++++++++++++++++++++++++++++++++++ | 68% ~08s
|+++++++++++++++++++++++++++++++++++ | 69% ~08s
|++++++++++++++++++++++++++++++++++++ | 70% ~08s
|++++++++++++++++++++++++++++++++++++ | 71% ~07s
|+++++++++++++++++++++++++++++++++++++ | 73% ~07s
|+++++++++++++++++++++++++++++++++++++ | 74% ~07s
|++++++++++++++++++++++++++++++++++++++ | 75% ~07s
|++++++++++++++++++++++++++++++++++++++ | 76% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 26s
|+ | 1 % ~40s
|++ | 2 % ~39s
|++ | 3 % ~38s
|+++ | 4 % ~38s
|+++ | 5 % ~38s
|++++ | 6 % ~37s
|++++ | 7 % ~36s
|+++++ | 8 % ~36s
|+++++ | 9 % ~36s
|++++++ | 10% ~35s
|++++++ | 11% ~35s
|+++++++ | 12% ~35s
|+++++++ | 13% ~34s
|++++++++ | 14% ~34s
|++++++++ | 15% ~33s
|+++++++++ | 16% ~33s
|+++++++++ | 17% ~33s
|++++++++++ | 18% ~32s
|++++++++++ | 19% ~32s
|+++++++++++ | 20% ~31s
|+++++++++++ | 21% ~31s
|++++++++++++ | 22% ~31s
|++++++++++++ | 23% ~30s
|+++++++++++++ | 24% ~30s
|+++++++++++++ | 25% ~30s
|++++++++++++++ | 26% ~29s
|++++++++++++++ | 27% ~29s
|+++++++++++++++ | 28% ~28s
|+++++++++++++++ | 29% ~28s
|++++++++++++++++ | 30% ~28s
|++++++++++++++++ | 31% ~27s
|+++++++++++++++++ | 32% ~27s
|+++++++++++++++++ | 33% ~26s
|++++++++++++++++++ | 34% ~26s
|++++++++++++++++++ | 35% ~26s
|+++++++++++++++++++ | 36% ~25s
|+++++++++++++++++++ | 37% ~25s
|++++++++++++++++++++ | 38% ~24s
|++++++++++++++++++++ | 39% ~24s
|+++++++++++++++++++++ | 40% ~24s
|+++++++++++++++++++++ | 41% ~23s
|++++++++++++++++++++++ | 42% ~23s
|++++++++++++++++++++++ | 43% ~22s
|+++++++++++++++++++++++ | 44% ~22s
|+++++++++++++++++++++++ | 45% ~22s
|++++++++++++++++++++++++ | 46% ~21s
|++++++++++++++++++++++++ | 47% ~21s
|+++++++++++++++++++++++++ | 48% ~20s
|+++++++++++++++++++++++++ | 49% ~20s
|++++++++++++++++++++++++++ | 51% ~20s
|++++++++++++++++++++++++++ | 52% ~19s
|+++++++++++++++++++++++++++ | 53% ~19s
|+++++++++++++++++++++++++++ | 54% ~18s
|++++++++++++++++++++++++++++ | 55% ~18s
|++++++++++++++++++++++++++++ | 56% ~18s
|+++++++++++++++++++++++++++++ | 57% ~17s
|+++++++++++++++++++++++++++++ | 58% ~17s
|++++++++++++++++++++++++++++++ | 59% ~16s
|++++++++++++++++++++++++++++++ | 60% ~16s
|+++++++++++++++++++++++++++++++ | 61% ~16s
|+++++++++++++++++++++++++++++++ | 62% ~15s
|++++++++++++++++++++++++++++++++ | 63% ~15s
|++++++++++++++++++++++++++++++++ | 64% ~14s
|+++++++++++++++++++++++++++++++++ | 65% ~14s
|+++++++++++++++++++++++++++++++++ | 66% ~14s
|++++++++++++++++++++++++++++++++++ | 67% ~13s
|++++++++++++++++++++++++++++++++++ | 68% ~13s
|+++++++++++++++++++++++++++++++++++ | 69% ~12s
|+++++++++++++++++++++++++++++++++++ | 70% ~12s
|++++++++++++++++++++++++++++++++++++ | 71% ~12s
|++++++++++++++++++++++++++++++++++++ | 72% ~11s
|+++++++++++++++++++++++++++++++++++++ | 73% ~11s
|+++++++++++++++++++++++++++++++++++++ | 74% ~10s
|++++++++++++++++++++++++++++++++++++++ | 75% ~10s
|++++++++++++++++++++++++++++++++++++++ | 76% ~10s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~09s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~09s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~08s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~08s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~08s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~07s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~07s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~06s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~06s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 39s
|+ | 1 % ~60s
|++ | 2 % ~59s
|++ | 3 % ~59s
|+++ | 4 % ~59s
|+++ | 5 % ~58s
|++++ | 6 % ~57s
|++++ | 7 % ~56s
|+++++ | 8 % ~55s
|+++++ | 9 % ~55s
|++++++ | 10% ~54s
|++++++ | 11% ~54s
|+++++++ | 12% ~53s
|+++++++ | 13% ~52s
|++++++++ | 14% ~52s
|++++++++ | 15% ~52s
|+++++++++ | 16% ~51s
|+++++++++ | 18% ~50s
|++++++++++ | 19% ~50s
|++++++++++ | 20% ~49s
|+++++++++++ | 21% ~48s
|+++++++++++ | 22% ~48s
|++++++++++++ | 23% ~47s
|++++++++++++ | 24% ~46s
|+++++++++++++ | 25% ~46s
|+++++++++++++ | 26% ~45s
|++++++++++++++ | 27% ~44s
|++++++++++++++ | 28% ~44s
|+++++++++++++++ | 29% ~43s
|+++++++++++++++ | 30% ~43s
|++++++++++++++++ | 31% ~42s
|++++++++++++++++ | 32% ~41s
|+++++++++++++++++ | 33% ~41s
|++++++++++++++++++ | 34% ~40s
|++++++++++++++++++ | 35% ~39s
|+++++++++++++++++++ | 36% ~39s
|+++++++++++++++++++ | 37% ~38s
|++++++++++++++++++++ | 38% ~37s
|++++++++++++++++++++ | 39% ~37s
|+++++++++++++++++++++ | 40% ~36s
|+++++++++++++++++++++ | 41% ~36s
|++++++++++++++++++++++ | 42% ~35s
|++++++++++++++++++++++ | 43% ~35s
|+++++++++++++++++++++++ | 44% ~34s
|+++++++++++++++++++++++ | 45% ~33s
|++++++++++++++++++++++++ | 46% ~33s
|++++++++++++++++++++++++ | 47% ~32s
|+++++++++++++++++++++++++ | 48% ~32s
|+++++++++++++++++++++++++ | 49% ~31s
|++++++++++++++++++++++++++ | 51% ~30s
|++++++++++++++++++++++++++ | 52% ~30s
|+++++++++++++++++++++++++++ | 53% ~29s
|+++++++++++++++++++++++++++ | 54% ~28s
|++++++++++++++++++++++++++++ | 55% ~28s
|++++++++++++++++++++++++++++ | 56% ~27s
|+++++++++++++++++++++++++++++ | 57% ~27s
|+++++++++++++++++++++++++++++ | 58% ~26s
|++++++++++++++++++++++++++++++ | 59% ~25s
|++++++++++++++++++++++++++++++ | 60% ~25s
|+++++++++++++++++++++++++++++++ | 61% ~24s
|+++++++++++++++++++++++++++++++ | 62% ~23s
|++++++++++++++++++++++++++++++++ | 63% ~23s
|++++++++++++++++++++++++++++++++ | 64% ~22s
|+++++++++++++++++++++++++++++++++ | 65% ~21s
|+++++++++++++++++++++++++++++++++ | 66% ~21s
|++++++++++++++++++++++++++++++++++ | 67% ~20s
|+++++++++++++++++++++++++++++++++++ | 68% ~20s
|+++++++++++++++++++++++++++++++++++ | 69% ~19s
|++++++++++++++++++++++++++++++++++++ | 70% ~18s
|++++++++++++++++++++++++++++++++++++ | 71% ~18s
|+++++++++++++++++++++++++++++++++++++ | 72% ~17s
|+++++++++++++++++++++++++++++++++++++ | 73% ~16s
|++++++++++++++++++++++++++++++++++++++ | 74% ~16s
|++++++++++++++++++++++++++++++++++++++ | 75% ~15s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~14s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~14s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~13s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~13s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~12s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~11s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~11s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~10s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~09s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~08s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 01s
|+ | 1 % ~01m 22s
|++ | 2 % ~01m 22s
|++ | 3 % ~01m 20s
|+++ | 4 % ~01m 19s
|+++ | 5 % ~01m 18s
|++++ | 6 % ~01m 18s
|++++ | 7 % ~01m 17s
|+++++ | 8 % ~01m 16s
|+++++ | 9 % ~01m 15s
|++++++ | 10% ~01m 14s
|++++++ | 11% ~01m 14s
|+++++++ | 12% ~01m 13s
|+++++++ | 13% ~01m 13s
|++++++++ | 14% ~01m 12s
|++++++++ | 15% ~01m 12s
|+++++++++ | 16% ~01m 11s
|+++++++++ | 17% ~01m 10s
|++++++++++ | 18% ~01m 09s
|++++++++++ | 19% ~01m 08s
|+++++++++++ | 20% ~01m 08s
|+++++++++++ | 21% ~01m 07s
|++++++++++++ | 22% ~01m 06s
|++++++++++++ | 23% ~01m 05s
|+++++++++++++ | 24% ~01m 04s
|+++++++++++++ | 26% ~01m 03s
|++++++++++++++ | 27% ~01m 02s
|++++++++++++++ | 28% ~01m 02s
|+++++++++++++++ | 29% ~01m 01s
|+++++++++++++++ | 30% ~60s
|++++++++++++++++ | 31% ~59s
|++++++++++++++++ | 32% ~58s
|+++++++++++++++++ | 33% ~57s
|+++++++++++++++++ | 34% ~56s
|++++++++++++++++++ | 35% ~55s
|++++++++++++++++++ | 36% ~55s
|+++++++++++++++++++ | 37% ~54s
|+++++++++++++++++++ | 38% ~53s
|++++++++++++++++++++ | 39% ~52s
|++++++++++++++++++++ | 40% ~51s
|+++++++++++++++++++++ | 41% ~50s
|+++++++++++++++++++++ | 42% ~50s
|++++++++++++++++++++++ | 43% ~49s
|++++++++++++++++++++++ | 44% ~48s
|+++++++++++++++++++++++ | 45% ~47s
|+++++++++++++++++++++++ | 46% ~46s
|++++++++++++++++++++++++ | 47% ~45s
|++++++++++++++++++++++++ | 48% ~44s
|+++++++++++++++++++++++++ | 49% ~43s
|+++++++++++++++++++++++++ | 50% ~43s
|++++++++++++++++++++++++++ | 51% ~42s
|+++++++++++++++++++++++++++ | 52% ~41s
|+++++++++++++++++++++++++++ | 53% ~40s
|++++++++++++++++++++++++++++ | 54% ~39s
|++++++++++++++++++++++++++++ | 55% ~38s
|+++++++++++++++++++++++++++++ | 56% ~37s
|+++++++++++++++++++++++++++++ | 57% ~37s
|++++++++++++++++++++++++++++++ | 58% ~36s
|++++++++++++++++++++++++++++++ | 59% ~35s
|+++++++++++++++++++++++++++++++ | 60% ~34s
|+++++++++++++++++++++++++++++++ | 61% ~33s
|++++++++++++++++++++++++++++++++ | 62% ~32s
|++++++++++++++++++++++++++++++++ | 63% ~31s
|+++++++++++++++++++++++++++++++++ | 64% ~31s
|+++++++++++++++++++++++++++++++++ | 65% ~30s
|++++++++++++++++++++++++++++++++++ | 66% ~29s
|++++++++++++++++++++++++++++++++++ | 67% ~28s
|+++++++++++++++++++++++++++++++++++ | 68% ~27s
|+++++++++++++++++++++++++++++++++++ | 69% ~26s
|++++++++++++++++++++++++++++++++++++ | 70% ~25s
|++++++++++++++++++++++++++++++++++++ | 71% ~25s
|+++++++++++++++++++++++++++++++++++++ | 72% ~24s
|+++++++++++++++++++++++++++++++++++++ | 73% ~23s
|++++++++++++++++++++++++++++++++++++++ | 74% ~22s
|++++++++++++++++++++++++++++++++++++++ | 76% ~21s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~20s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~19s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~18s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~18s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~17s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~16s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~15s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~14s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 26s
|+ | 1 % ~02m 30s
|++ | 2 % ~02m 29s
|++ | 3 % ~02m 27s
|+++ | 4 % ~02m 27s
|+++ | 5 % ~02m 25s
|++++ | 6 % ~02m 23s
|++++ | 7 % ~02m 21s
|+++++ | 8 % ~02m 20s
|+++++ | 9 % ~02m 18s
|++++++ | 10% ~02m 17s
|++++++ | 11% ~02m 15s
|+++++++ | 12% ~02m 14s
|+++++++ | 13% ~02m 12s
|++++++++ | 14% ~02m 12s
|++++++++ | 15% ~02m 10s
|+++++++++ | 16% ~02m 08s
|+++++++++ | 17% ~02m 07s
|++++++++++ | 18% ~02m 05s
|++++++++++ | 19% ~02m 04s
|+++++++++++ | 20% ~02m 02s
|+++++++++++ | 21% ~02m 01s
|++++++++++++ | 22% ~01m 59s
|++++++++++++ | 23% ~01m 58s
|+++++++++++++ | 24% ~01m 56s
|+++++++++++++ | 25% ~01m 55s
|++++++++++++++ | 26% ~01m 53s
|++++++++++++++ | 27% ~01m 52s
|+++++++++++++++ | 28% ~01m 50s
|+++++++++++++++ | 29% ~01m 49s
|++++++++++++++++ | 30% ~01m 47s
|++++++++++++++++ | 31% ~01m 45s
|+++++++++++++++++ | 32% ~01m 44s
|+++++++++++++++++ | 33% ~01m 43s
|++++++++++++++++++ | 34% ~01m 41s
|++++++++++++++++++ | 35% ~01m 39s
|+++++++++++++++++++ | 36% ~01m 38s
|+++++++++++++++++++ | 37% ~01m 36s
|++++++++++++++++++++ | 38% ~01m 35s
|++++++++++++++++++++ | 39% ~01m 35s
|+++++++++++++++++++++ | 40% ~01m 33s
|+++++++++++++++++++++ | 41% ~01m 32s
|++++++++++++++++++++++ | 42% ~01m 30s
|++++++++++++++++++++++ | 43% ~01m 28s
|+++++++++++++++++++++++ | 44% ~01m 27s
|+++++++++++++++++++++++ | 45% ~01m 25s
|++++++++++++++++++++++++ | 46% ~01m 24s
|++++++++++++++++++++++++ | 47% ~01m 22s
|+++++++++++++++++++++++++ | 48% ~01m 21s
|+++++++++++++++++++++++++ | 49% ~01m 19s
|++++++++++++++++++++++++++ | 51% ~01m 17s
|++++++++++++++++++++++++++ | 52% ~01m 16s
|+++++++++++++++++++++++++++ | 53% ~01m 14s
|+++++++++++++++++++++++++++ | 54% ~01m 13s
|++++++++++++++++++++++++++++ | 55% ~01m 11s
|++++++++++++++++++++++++++++ | 56% ~01m 10s
|+++++++++++++++++++++++++++++ | 57% ~01m 08s
|+++++++++++++++++++++++++++++ | 58% ~01m 06s
|++++++++++++++++++++++++++++++ | 59% ~01m 05s
|++++++++++++++++++++++++++++++ | 60% ~01m 03s
|+++++++++++++++++++++++++++++++ | 61% ~01m 02s
|+++++++++++++++++++++++++++++++ | 62% ~01m 01s
|++++++++++++++++++++++++++++++++ | 63% ~59s
|++++++++++++++++++++++++++++++++ | 64% ~58s
|+++++++++++++++++++++++++++++++++ | 65% ~56s
|+++++++++++++++++++++++++++++++++ | 66% ~54s
|++++++++++++++++++++++++++++++++++ | 67% ~53s
|++++++++++++++++++++++++++++++++++ | 68% ~51s
|+++++++++++++++++++++++++++++++++++ | 69% ~49s
|+++++++++++++++++++++++++++++++++++ | 70% ~48s
|++++++++++++++++++++++++++++++++++++ | 71% ~46s
|++++++++++++++++++++++++++++++++++++ | 72% ~45s
|+++++++++++++++++++++++++++++++++++++ | 73% ~43s
|+++++++++++++++++++++++++++++++++++++ | 74% ~41s
|++++++++++++++++++++++++++++++++++++++ | 75% ~40s
|++++++++++++++++++++++++++++++++++++++ | 76% ~38s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~37s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~35s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~33s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~32s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~30s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~29s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~27s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~25s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~24s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~22s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~21s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~19s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~17s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~16s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~14s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~10s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 36s
|+ | 1 % ~01m 51s
|++ | 2 % ~01m 51s
|++ | 3 % ~01m 50s
|+++ | 4 % ~01m 52s
|+++ | 5 % ~01m 50s
|++++ | 6 % ~01m 49s
|++++ | 7 % ~01m 47s
|+++++ | 8 % ~01m 46s
|+++++ | 9 % ~01m 45s
|++++++ | 10% ~01m 43s
|++++++ | 11% ~01m 42s
|+++++++ | 12% ~01m 41s
|+++++++ | 13% ~01m 41s
|++++++++ | 14% ~01m 40s
|++++++++ | 15% ~01m 38s
|+++++++++ | 16% ~01m 37s
|+++++++++ | 18% ~01m 36s
|++++++++++ | 19% ~01m 34s
|++++++++++ | 20% ~01m 33s
|+++++++++++ | 21% ~01m 32s
|+++++++++++ | 22% ~01m 31s
|++++++++++++ | 23% ~01m 32s
|++++++++++++ | 24% ~01m 30s
|+++++++++++++ | 25% ~01m 29s
|+++++++++++++ | 26% ~01m 28s
|++++++++++++++ | 27% ~01m 26s
|++++++++++++++ | 28% ~01m 25s
|+++++++++++++++ | 29% ~01m 24s
|+++++++++++++++ | 30% ~01m 22s
|++++++++++++++++ | 31% ~01m 21s
|++++++++++++++++ | 32% ~01m 20s
|+++++++++++++++++ | 33% ~01m 19s
|++++++++++++++++++ | 34% ~01m 18s
|++++++++++++++++++ | 35% ~01m 16s
|+++++++++++++++++++ | 36% ~01m 15s
|+++++++++++++++++++ | 37% ~01m 14s
|++++++++++++++++++++ | 38% ~01m 13s
|++++++++++++++++++++ | 39% ~01m 11s
|+++++++++++++++++++++ | 40% ~01m 10s
|+++++++++++++++++++++ | 41% ~01m 09s
|++++++++++++++++++++++ | 42% ~01m 08s
|++++++++++++++++++++++ | 43% ~01m 07s
|+++++++++++++++++++++++ | 44% ~01m 06s
|+++++++++++++++++++++++ | 45% ~01m 04s
|++++++++++++++++++++++++ | 46% ~01m 03s
|++++++++++++++++++++++++ | 47% ~01m 02s
|+++++++++++++++++++++++++ | 48% ~01m 01s
|+++++++++++++++++++++++++ | 49% ~59s
|++++++++++++++++++++++++++ | 51% ~59s
|++++++++++++++++++++++++++ | 52% ~58s
|+++++++++++++++++++++++++++ | 53% ~57s
|+++++++++++++++++++++++++++ | 54% ~55s
|++++++++++++++++++++++++++++ | 55% ~54s
|++++++++++++++++++++++++++++ | 56% ~53s
|+++++++++++++++++++++++++++++ | 57% ~52s
|+++++++++++++++++++++++++++++ | 58% ~50s
|++++++++++++++++++++++++++++++ | 59% ~49s
|++++++++++++++++++++++++++++++ | 60% ~48s
|+++++++++++++++++++++++++++++++ | 61% ~47s
|+++++++++++++++++++++++++++++++ | 62% ~45s
|++++++++++++++++++++++++++++++++ | 63% ~44s
|++++++++++++++++++++++++++++++++ | 64% ~43s
|+++++++++++++++++++++++++++++++++ | 65% ~42s
|+++++++++++++++++++++++++++++++++ | 66% ~40s
|++++++++++++++++++++++++++++++++++ | 67% ~39s
|+++++++++++++++++++++++++++++++++++ | 68% ~38s
|+++++++++++++++++++++++++++++++++++ | 69% ~37s
|++++++++++++++++++++++++++++++++++++ | 70% ~35s
|++++++++++++++++++++++++++++++++++++ | 71% ~34s
|+++++++++++++++++++++++++++++++++++++ | 72% ~33s
|+++++++++++++++++++++++++++++++++++++ | 73% ~32s
|++++++++++++++++++++++++++++++++++++++ | 74% ~31s
|++++++++++++++++++++++++++++++++++++++ | 75% ~29s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~28s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~27s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~26s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~24s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~23s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~22s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~21s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~20s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~18s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~17s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~16s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~12s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 58s
|+ | 1 % ~02m 15s
|+ | 2 % ~02m 14s
|++ | 3 % ~02m 13s
|++ | 4 % ~02m 14s
|+++ | 5 % ~02m 12s
|+++ | 6 % ~02m 10s
|++++ | 7 % ~02m 08s
|++++ | 8 % ~02m 07s
|+++++ | 9 % ~02m 05s
|+++++ | 10% ~02m 04s
|++++++ | 11% ~02m 02s
|++++++ | 12% ~02m 01s
|+++++++ | 13% ~02m 01s
|+++++++ | 14% ~02m 01s
|++++++++ | 15% ~01m 59s
|++++++++ | 16% ~01m 58s
|+++++++++ | 17% ~01m 56s
|+++++++++ | 18% ~01m 55s
|++++++++++ | 19% ~01m 53s
|++++++++++ | 20% ~01m 52s
|+++++++++++ | 21% ~01m 55s
|+++++++++++ | 22% ~01m 53s
|++++++++++++ | 23% ~01m 51s
|++++++++++++ | 24% ~01m 50s
|+++++++++++++ | 25% ~01m 48s
|+++++++++++++ | 26% ~01m 46s
|++++++++++++++ | 27% ~01m 45s
|++++++++++++++ | 28% ~01m 43s
|+++++++++++++++ | 29% ~01m 42s
|+++++++++++++++ | 30% ~01m 40s
|++++++++++++++++ | 31% ~01m 38s
|++++++++++++++++ | 32% ~01m 37s
|+++++++++++++++++ | 33% ~01m 35s
|+++++++++++++++++ | 34% ~01m 34s
|++++++++++++++++++ | 35% ~01m 33s
|++++++++++++++++++ | 36% ~01m 31s
|+++++++++++++++++++ | 37% ~01m 30s
|+++++++++++++++++++ | 38% ~01m 28s
|++++++++++++++++++++ | 39% ~01m 27s
|++++++++++++++++++++ | 40% ~01m 25s
|+++++++++++++++++++++ | 41% ~01m 24s
|+++++++++++++++++++++ | 42% ~01m 22s
|++++++++++++++++++++++ | 43% ~01m 21s
|++++++++++++++++++++++ | 44% ~01m 19s
|+++++++++++++++++++++++ | 45% ~01m 18s
|+++++++++++++++++++++++ | 46% ~01m 16s
|++++++++++++++++++++++++ | 47% ~01m 15s
|++++++++++++++++++++++++ | 48% ~01m 14s
|+++++++++++++++++++++++++ | 49% ~01m 12s
|+++++++++++++++++++++++++ | 50% ~01m 11s
|++++++++++++++++++++++++++ | 51% ~01m 09s
|++++++++++++++++++++++++++ | 52% ~01m 08s
|+++++++++++++++++++++++++++ | 53% ~01m 06s
|+++++++++++++++++++++++++++ | 54% ~01m 05s
|++++++++++++++++++++++++++++ | 55% ~01m 03s
|++++++++++++++++++++++++++++ | 56% ~01m 02s
|+++++++++++++++++++++++++++++ | 57% ~01m 01s
|+++++++++++++++++++++++++++++ | 58% ~59s
|++++++++++++++++++++++++++++++ | 59% ~58s
|++++++++++++++++++++++++++++++ | 60% ~56s
|+++++++++++++++++++++++++++++++ | 61% ~55s
|+++++++++++++++++++++++++++++++ | 62% ~53s
|++++++++++++++++++++++++++++++++ | 63% ~52s
|++++++++++++++++++++++++++++++++ | 64% ~51s
|+++++++++++++++++++++++++++++++++ | 65% ~49s
|+++++++++++++++++++++++++++++++++ | 66% ~48s
|++++++++++++++++++++++++++++++++++ | 67% ~46s
|++++++++++++++++++++++++++++++++++ | 68% ~45s
|+++++++++++++++++++++++++++++++++++ | 69% ~44s
|+++++++++++++++++++++++++++++++++++ | 70% ~42s
|++++++++++++++++++++++++++++++++++++ | 71% ~41s
|++++++++++++++++++++++++++++++++++++ | 72% ~39s
|+++++++++++++++++++++++++++++++++++++ | 73% ~38s
|+++++++++++++++++++++++++++++++++++++ | 74% ~36s
|++++++++++++++++++++++++++++++++++++++ | 75% ~35s
|++++++++++++++++++++++++++++++++++++++ | 76% ~34s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~32s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~31s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~30s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~28s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~27s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~25s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~24s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~22s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~21s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~20s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~18s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~17s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~14s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~13s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 22s
|+ | 1 % ~01m 17s
|++ | 2 % ~01m 16s
|++ | 3 % ~01m 16s
|+++ | 4 % ~01m 15s
|+++ | 5 % ~01m 14s
|++++ | 6 % ~01m 13s
|++++ | 7 % ~01m 12s
|+++++ | 8 % ~01m 12s
|+++++ | 9 % ~01m 11s
|++++++ | 10% ~01m 10s
|++++++ | 11% ~01m 09s
|+++++++ | 12% ~01m 09s
|+++++++ | 13% ~01m 09s
|++++++++ | 14% ~01m 08s
|++++++++ | 15% ~01m 07s
|+++++++++ | 16% ~01m 06s
|+++++++++ | 18% ~01m 05s
|++++++++++ | 19% ~01m 04s
|++++++++++ | 20% ~01m 04s
|+++++++++++ | 21% ~01m 03s
|+++++++++++ | 22% ~01m 02s
|++++++++++++ | 23% ~01m 01s
|++++++++++++ | 24% ~01m 00s
|+++++++++++++ | 25% ~60s
|+++++++++++++ | 26% ~59s
|++++++++++++++ | 27% ~58s
|++++++++++++++ | 28% ~57s
|+++++++++++++++ | 29% ~56s
|+++++++++++++++ | 30% ~55s
|++++++++++++++++ | 31% ~55s
|++++++++++++++++ | 32% ~54s
|+++++++++++++++++ | 33% ~53s
|++++++++++++++++++ | 34% ~52s
|++++++++++++++++++ | 35% ~52s
|+++++++++++++++++++ | 36% ~51s
|+++++++++++++++++++ | 37% ~50s
|++++++++++++++++++++ | 38% ~49s
|++++++++++++++++++++ | 39% ~48s
|+++++++++++++++++++++ | 40% ~47s
|+++++++++++++++++++++ | 41% ~47s
|++++++++++++++++++++++ | 42% ~46s
|++++++++++++++++++++++ | 43% ~45s
|+++++++++++++++++++++++ | 44% ~44s
|+++++++++++++++++++++++ | 45% ~43s
|++++++++++++++++++++++++ | 46% ~42s
|++++++++++++++++++++++++ | 47% ~42s
|+++++++++++++++++++++++++ | 48% ~41s
|+++++++++++++++++++++++++ | 49% ~40s
|++++++++++++++++++++++++++ | 51% ~39s
|++++++++++++++++++++++++++ | 52% ~38s
|+++++++++++++++++++++++++++ | 53% ~38s
|+++++++++++++++++++++++++++ | 54% ~37s
|++++++++++++++++++++++++++++ | 55% ~36s
|++++++++++++++++++++++++++++ | 56% ~35s
|+++++++++++++++++++++++++++++ | 57% ~34s
|+++++++++++++++++++++++++++++ | 58% ~33s
|++++++++++++++++++++++++++++++ | 59% ~33s
|++++++++++++++++++++++++++++++ | 60% ~32s
|+++++++++++++++++++++++++++++++ | 61% ~31s
|+++++++++++++++++++++++++++++++ | 62% ~30s
|++++++++++++++++++++++++++++++++ | 63% ~29s
|++++++++++++++++++++++++++++++++ | 64% ~29s
|+++++++++++++++++++++++++++++++++ | 65% ~28s
|+++++++++++++++++++++++++++++++++ | 66% ~27s
|++++++++++++++++++++++++++++++++++ | 67% ~26s
|+++++++++++++++++++++++++++++++++++ | 68% ~25s
|+++++++++++++++++++++++++++++++++++ | 69% ~25s
|++++++++++++++++++++++++++++++++++++ | 70% ~24s
|++++++++++++++++++++++++++++++++++++ | 71% ~23s
|+++++++++++++++++++++++++++++++++++++ | 72% ~22s
|+++++++++++++++++++++++++++++++++++++ | 73% ~21s
|++++++++++++++++++++++++++++++++++++++ | 74% ~20s
|++++++++++++++++++++++++++++++++++++++ | 75% ~20s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~19s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~18s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~16s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~16s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~15s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~12s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 20s
|+ | 1 % ~50s
|++ | 2 % ~49s
|++ | 3 % ~49s
|+++ | 4 % ~49s
|+++ | 5 % ~48s
|++++ | 7 % ~48s
|++++ | 8 % ~47s
|+++++ | 9 % ~46s
|+++++ | 10% ~47s
|++++++ | 11% ~46s
|++++++ | 12% ~45s
|+++++++ | 13% ~45s
|++++++++ | 14% ~44s
|++++++++ | 15% ~44s
|+++++++++ | 16% ~43s
|+++++++++ | 17% ~42s
|++++++++++ | 18% ~42s
|++++++++++ | 20% ~41s
|+++++++++++ | 21% ~41s
|+++++++++++ | 22% ~40s
|++++++++++++ | 23% ~39s
|++++++++++++ | 24% ~39s
|+++++++++++++ | 25% ~38s
|++++++++++++++ | 26% ~38s
|++++++++++++++ | 27% ~37s
|+++++++++++++++ | 28% ~37s
|+++++++++++++++ | 29% ~36s
|++++++++++++++++ | 30% ~36s
|++++++++++++++++ | 32% ~35s
|+++++++++++++++++ | 33% ~34s
|+++++++++++++++++ | 34% ~34s
|++++++++++++++++++ | 35% ~33s
|++++++++++++++++++ | 36% ~33s
|+++++++++++++++++++ | 37% ~32s
|++++++++++++++++++++ | 38% ~32s
|++++++++++++++++++++ | 39% ~31s
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|+++++++++++++++++++++ | 41% ~30s
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|++++++++++++++++++++++ | 43% ~29s
|+++++++++++++++++++++++ | 45% ~29s
|+++++++++++++++++++++++ | 46% ~28s
|++++++++++++++++++++++++ | 47% ~27s
|++++++++++++++++++++++++ | 48% ~27s
|+++++++++++++++++++++++++ | 49% ~26s
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|++++++++++++++++++++++++++ | 51% ~25s
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|+++++++++++++++++++++++++++ | 53% ~24s
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|++++++++++++++++++++++++++++ | 55% ~23s
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|++++++++++++++++++++++++++++++++ | 63% ~19s
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|++++++++++++++++++++++++++++++++++ | 67% ~17s
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|+++++++++++++++++++++++++++++++++++ | 70% ~16s
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|+++++++++++++++++++++++++++++++++++++ | 73% ~14s
|+++++++++++++++++++++++++++++++++++++ | 74% ~13s
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|+++++++++++++++++++++++++++++++++++++++ | 76% ~12s
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|++++++++++++++++++++++++++++++++++++++++ | 78% ~11s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~11s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~10s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~10s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~09s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~08s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~08s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~07s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 52s
Color the tSNE plot by the age of the animals
x <- FeaturePlot(object = seurat_10X2 , features.plot = "age_num" , cols.use = c("slateblue","yellowgreen") , no.legend = FALSE , do.return = TRUE )
tsne_ageplot <- ggplot(data = x$age_num$data , mapping = aes(x = x , y = y , color = col)) + geom_point(size = 1) + scale_color_manual(values = c("slateblue","yellowgreen") , labels = c( "old" , "young" ) , name = "age of animal" ) + labs( x = "tSNE_1" , y = "tSNE_2" ) + coord_equal()
plotsize = 5
tsne_ageplot
#ggsave(plot = tsne_ageplot , filename = "age_tsne_hires.png" , width = plotsize , height = plotsize )
ggplot(data = x$age_num$data , mapping = aes(x = x , y = y , color = col)) + geom_point(size = 1) + scale_color_manual(values = c("slateblue","yellowgreen") , labels = c( "old" , "young" ) , name = "age of animal" ) + labs( x = "tSNE_1" , y = "tSNE_2" ) + coord_equal() # + guides(color = "none")
Color the tSNE by the identified celltypes and subpopulations
g <- TSNEPlot(seurat_10X2 , do.return = TRUE)
ggplot(data = g$data , mapping = aes(x = x , y = y , color = ident)) +
geom_point(size = 1) +
labs( x = "tSNE_1" , y = "tSNE_2" ) +
coord_equal() +
scale_color_manual(values = c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC0 = "tomato" , aNSC1 = "sienna1", aNSC2 = "sienna3" , TAP = "green" , NB = "yellow" , OPC = "pink" , OD = "violet") , name = "Type" )
ggplot(data = g$data , mapping = aes(x = x , y = y , color = ident)) +
geom_point(size = 1) +
labs( x = "tSNE_1" , y = "tSNE_2" ) +
coord_equal() +
# guides(color = "none") +
scale_color_manual(values = c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC0 = "tomato" , aNSC1 = "sienna1", aNSC2 = "sienna3" , TAP = "green" , NB = "yellow" , OPC = "pink" , OD = "violet") , name = "Type" )
# saveRDS(object = seurat_10X2 , file = "seurat_10X2_clustered_min_1500_nGene_all_cells.RDS")
We find that there are two small clusters in the tSNE plot that lie outside the qNSC2 and the aNSC0. Using the interactive visualisation feature we can determine the names of these cells and check for differentially expressed genes from those.
outside_q2 <- c("GCGAGAATCGCTTAGA-1", "GTTAAGCGTGCACTTA-1", "TCATTTGTCGTCCAGG-1",
"AACGTTGCACGCCAGT-2", "CAACTAGAGTCGCCGT-2", "CCACTACTCGGAATCT-2",
"CCGTTCATCCTGCTTG-2", "CGCTGGAGTAGTACCT-2", "TACTTACTCCTAGGGC-2",
"TAGTTGGCACATTAGC-2", "TGGCTGGCACTCAGGC-2", "TGTATTCCATGTTGAC-2"
)
outside_a0 <- c("ATGGGAGAGATCCGAG-1", "TACACGACAAAGTGCG-1", "CCATGTCTCCTAGGGC-2",
"CGTGAGCCACCATGTA-2", "CTCGAAAAGTGCGATG-2", "TGACGGCCATCGATGT-2"
)
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = outside_a0 , ident.use = "outside_a0")
seurat_10X2 <- SetIdent(object = seurat_10X2 , cells.use = outside_q2 , ident.use = "outside_q2")
TSNEPlot(object = seurat_10X2 , do.label = TRUE)
First the cells outside aNSC0
outside_a0_markers <- FindMarkers(object = seurat_10X2 , ident.1 = "outside_a0")
|+ | 1 % ~05m 26s
|+ | 2 % ~05m 19s
|++ | 3 % ~05m 15s
|++ | 4 % ~05m 13s
|+++ | 5 % ~05m 08s
|+++ | 6 % ~05m 03s
|++++ | 7 % ~04m 59s
|++++ | 8 % ~04m 56s
|+++++ | 9 % ~04m 53s
|+++++ | 10% ~04m 49s
|++++++ | 11% ~04m 47s
|++++++ | 12% ~04m 45s
|+++++++ | 13% ~04m 41s
|+++++++ | 14% ~04m 38s
|++++++++ | 15% ~04m 35s
|++++++++ | 16% ~04m 31s
|+++++++++ | 17% ~04m 28s
|+++++++++ | 18% ~04m 25s
|++++++++++ | 19% ~04m 22s
|++++++++++ | 20% ~04m 19s
|+++++++++++ | 21% ~04m 16s
|+++++++++++ | 22% ~04m 13s
|++++++++++++ | 23% ~04m 09s
|++++++++++++ | 24% ~04m 06s
|+++++++++++++ | 25% ~04m 03s
|+++++++++++++ | 26% ~03m 60s
|++++++++++++++ | 27% ~03m 56s
|++++++++++++++ | 28% ~03m 53s
|+++++++++++++++ | 29% ~03m 50s
|+++++++++++++++ | 30% ~03m 47s
|++++++++++++++++ | 31% ~03m 44s
|++++++++++++++++ | 32% ~03m 40s
|+++++++++++++++++ | 33% ~03m 37s
|+++++++++++++++++ | 34% ~03m 34s
|++++++++++++++++++ | 35% ~03m 31s
|++++++++++++++++++ | 36% ~03m 27s
|+++++++++++++++++++ | 37% ~03m 24s
|+++++++++++++++++++ | 38% ~03m 21s
|++++++++++++++++++++ | 39% ~03m 17s
|++++++++++++++++++++ | 40% ~03m 14s
|+++++++++++++++++++++ | 41% ~03m 11s
|+++++++++++++++++++++ | 42% ~03m 08s
|++++++++++++++++++++++ | 43% ~03m 05s
|++++++++++++++++++++++ | 44% ~03m 01s
|+++++++++++++++++++++++ | 45% ~02m 58s
|+++++++++++++++++++++++ | 46% ~02m 55s
|++++++++++++++++++++++++ | 47% ~02m 52s
|++++++++++++++++++++++++ | 48% ~02m 49s
|+++++++++++++++++++++++++ | 49% ~02m 46s
|+++++++++++++++++++++++++ | 50% ~02m 43s
|++++++++++++++++++++++++++ | 51% ~02m 39s
|++++++++++++++++++++++++++ | 52% ~02m 36s
|+++++++++++++++++++++++++++ | 53% ~02m 33s
|+++++++++++++++++++++++++++ | 54% ~02m 30s
|++++++++++++++++++++++++++++ | 55% ~02m 27s
|++++++++++++++++++++++++++++ | 56% ~02m 23s
|+++++++++++++++++++++++++++++ | 57% ~02m 20s
|+++++++++++++++++++++++++++++ | 58% ~02m 17s
|++++++++++++++++++++++++++++++ | 59% ~02m 13s
|++++++++++++++++++++++++++++++ | 60% ~02m 10s
|+++++++++++++++++++++++++++++++ | 61% ~02m 07s
|+++++++++++++++++++++++++++++++ | 62% ~02m 04s
|++++++++++++++++++++++++++++++++ | 63% ~02m 00s
|++++++++++++++++++++++++++++++++ | 64% ~01m 57s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 54s
|+++++++++++++++++++++++++++++++++ | 66% ~01m 51s
|++++++++++++++++++++++++++++++++++ | 67% ~01m 47s
|++++++++++++++++++++++++++++++++++ | 68% ~01m 44s
|+++++++++++++++++++++++++++++++++++ | 69% ~01m 41s
|+++++++++++++++++++++++++++++++++++ | 70% ~01m 38s
|++++++++++++++++++++++++++++++++++++ | 71% ~01m 34s
|++++++++++++++++++++++++++++++++++++ | 72% ~01m 31s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01m 28s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01m 25s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01m 21s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01m 18s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01m 15s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01m 12s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 08s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01m 05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01m 02s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~59s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~55s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~52s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~49s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~46s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~42s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~39s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~36s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~33s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~29s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~26s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~23s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~20s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~16s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~13s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 05m 25s
outside_a0_markers
Checking the differentially expressed genes we can see many leukocyte marker genes, amongst them CD45.
## Ptprc = CD45 -> Leukocyte Common Antigen (LCA)
VlnPlot(object = seurat_10X2 , features.plot = c("Ptprc","Slc1a3") , x.lab.rot = TRUE )
Next the cells outside qNSC2
outside_q2_markers <- FindMarkers(object = seurat_10X2 , ident.1 = "outside_q2")
|+ | 1 % ~03m 40s
|+ | 2 % ~03m 41s
|++ | 3 % ~03m 37s
|++ | 4 % ~03m 34s
|+++ | 5 % ~03m 31s
|+++ | 6 % ~03m 29s
|++++ | 7 % ~03m 26s
|++++ | 8 % ~03m 24s
|+++++ | 9 % ~03m 22s
|+++++ | 10% ~03m 19s
|++++++ | 11% ~03m 18s
|++++++ | 12% ~03m 15s
|+++++++ | 13% ~03m 13s
|+++++++ | 14% ~03m 11s
|++++++++ | 15% ~03m 09s
|++++++++ | 16% ~03m 07s
|+++++++++ | 17% ~03m 04s
|+++++++++ | 18% ~03m 02s
|++++++++++ | 19% ~03m 01s
|++++++++++ | 20% ~02m 59s
|+++++++++++ | 21% ~02m 56s
|+++++++++++ | 22% ~02m 54s
|++++++++++++ | 23% ~02m 52s
|++++++++++++ | 24% ~02m 49s
|+++++++++++++ | 25% ~02m 47s
|+++++++++++++ | 26% ~02m 45s
|++++++++++++++ | 27% ~02m 43s
|++++++++++++++ | 28% ~02m 41s
|+++++++++++++++ | 29% ~02m 38s
|+++++++++++++++ | 30% ~02m 36s
|++++++++++++++++ | 31% ~02m 34s
|++++++++++++++++ | 32% ~02m 31s
|+++++++++++++++++ | 33% ~02m 29s
|+++++++++++++++++ | 34% ~02m 27s
|++++++++++++++++++ | 35% ~02m 25s
|++++++++++++++++++ | 36% ~02m 23s
|+++++++++++++++++++ | 37% ~02m 20s
|+++++++++++++++++++ | 38% ~02m 18s
|++++++++++++++++++++ | 39% ~02m 16s
|++++++++++++++++++++ | 40% ~02m 14s
|+++++++++++++++++++++ | 41% ~02m 12s
|+++++++++++++++++++++ | 42% ~02m 09s
|++++++++++++++++++++++ | 43% ~02m 07s
|++++++++++++++++++++++ | 44% ~02m 05s
|+++++++++++++++++++++++ | 45% ~02m 03s
|+++++++++++++++++++++++ | 46% ~02m 01s
|++++++++++++++++++++++++ | 47% ~01m 58s
|++++++++++++++++++++++++ | 48% ~01m 56s
|+++++++++++++++++++++++++ | 49% ~01m 54s
|+++++++++++++++++++++++++ | 50% ~01m 52s
|++++++++++++++++++++++++++ | 51% ~01m 50s
|++++++++++++++++++++++++++ | 52% ~01m 48s
|+++++++++++++++++++++++++++ | 53% ~01m 45s
|+++++++++++++++++++++++++++ | 54% ~01m 43s
|++++++++++++++++++++++++++++ | 55% ~01m 41s
|++++++++++++++++++++++++++++ | 56% ~01m 39s
|+++++++++++++++++++++++++++++ | 57% ~01m 37s
|+++++++++++++++++++++++++++++ | 58% ~01m 34s
|++++++++++++++++++++++++++++++ | 59% ~01m 32s
|++++++++++++++++++++++++++++++ | 60% ~01m 30s
|+++++++++++++++++++++++++++++++ | 61% ~01m 28s
|+++++++++++++++++++++++++++++++ | 62% ~01m 25s
|++++++++++++++++++++++++++++++++ | 63% ~01m 23s
|++++++++++++++++++++++++++++++++ | 64% ~01m 21s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 19s
|+++++++++++++++++++++++++++++++++ | 66% ~01m 16s
|++++++++++++++++++++++++++++++++++ | 67% ~01m 15s
|++++++++++++++++++++++++++++++++++ | 68% ~01m 12s
|+++++++++++++++++++++++++++++++++++ | 69% ~01m 10s
|+++++++++++++++++++++++++++++++++++ | 70% ~01m 08s
|++++++++++++++++++++++++++++++++++++ | 71% ~01m 05s
|++++++++++++++++++++++++++++++++++++ | 72% ~01m 03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01m 01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~59s
|++++++++++++++++++++++++++++++++++++++ | 75% ~56s
|++++++++++++++++++++++++++++++++++++++ | 76% ~54s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~52s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~50s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~47s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~45s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~43s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~40s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~38s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~36s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~34s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~31s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~29s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~27s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~25s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~20s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~18s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~16s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~13s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03m 42s
outside_q2_markers
Checking the differentially expressed genes we can see ependymal cell genes (Genes from https://pdfs.semanticscholar.org/e833/5f8154f714088147d76701ee0052d3a388b5.pdf)
VlnPlot(object = seurat_10X2 , features.plot = c("Iqca","Bbox1","Apoe","Syne1","Meig1","Foxj1","Slc1a3") , x.lab.rot = TRUE )
Next we exclude these contaminating cells and save the new seurat objects as a RDS file.
cells_to_remove <- WhichCells(object = seurat_10X2 , ident = c("outside_q2","outside_a0"))
cells_to_remove
[1] "GCGAGAATCGCTTAGA-1" "GTTAAGCGTGCACTTA-1" "TCATTTGTCGTCCAGG-1"
[4] "AACGTTGCACGCCAGT-2" "CAACTAGAGTCGCCGT-2" "CCACTACTCGGAATCT-2"
[7] "CCGTTCATCCTGCTTG-2" "CGCTGGAGTAGTACCT-2" "TACTTACTCCTAGGGC-2"
[10] "TAGTTGGCACATTAGC-2" "TGGCTGGCACTCAGGC-2" "TGTATTCCATGTTGAC-2"
[13] "ATGGGAGAGATCCGAG-1" "TACACGACAAAGTGCG-1" "CCATGTCTCCTAGGGC-2"
[16] "CGTGAGCCACCATGTA-2" "CTCGAAAAGTGCGATG-2" "TGACGGCCATCGATGT-2"
After we made sure we know which kind of celltype these cells are, we can get rid of them in the following analysis
all_cells <- WhichCells(object = seurat_10X2 )
cells_to_keep <- all_cells[! all_cells %in% cells_to_remove]
seurat_10X2 <- SubsetData(object = seurat_10X2 , cells.use = cells_to_keep )
TSNEPlot(object = seurat_10X2 , do.label = TRUE)
# saveRDS(object = seurat_10X2 , file = "seurat_10X2_clustered_min_1500_nGene.RDS")
SessionInfo
sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.3 LTS
Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets methods
[9] base
other attached packages:
[1] org.Mm.eg.db_3.5.0 AnnotationDbi_1.40.0 IRanges_2.12.0
[4] S4Vectors_0.16.0 bindrcpp_0.2 ReactomePA_1.22.0
[7] clusterProfiler_3.4.4 DOSE_3.4.0 RColorBrewer_1.1-2
[10] pheatmap_1.0.10 kableExtra_0.9.0 knitr_1.20
[13] Seurat_2.2.0 Matrix_1.2-14 cowplot_0.7.0
[16] forcats_0.3.0 stringr_1.3.1 dplyr_0.7.4
[19] purrr_0.2.4 readr_1.1.1 tidyr_0.7.2
[22] tibble_1.4.2 ggplot2_2.2.1 tidyverse_1.2.1
[25] Biobase_2.38.0 BiocGenerics_0.24.0
loaded via a namespace (and not attached):
[1] utf8_1.1.4 R.utils_2.6.0 lme4_1.1-18-1
[4] RSQLite_2.1.1 htmlwidgets_1.2 grid_3.4.3
[7] trimcluster_0.1-2 ranger_0.6.0 BiocParallel_1.12.0
[10] Rtsne_0.11 munsell_0.4.3 codetools_0.2-15
[13] ica_1.0-2 colorspace_1.3-2 GOSemSim_2.4.1
[16] rstudioapi_0.7 ROCR_1.0-7 robustbase_0.92-7
[19] dtw_1.20-1 NMF_0.20.6 labeling_0.3
[22] lars_1.2 mnormt_1.5-5 bit64_0.9-7
[25] rprojroot_1.3-2 diptest_0.75-7 R6_2.2.2
[28] doParallel_1.0.10 VGAM_1.0-3 flexmix_2.3-13
[31] bitops_1.0-6 fgsea_1.4.1 assertthat_0.2.0
[34] SDMTools_1.1-221 scales_0.5.0 nnet_7.3-12
[37] gtable_0.2.0 rlang_0.2.2 MatrixModels_0.4-1
[40] scatterplot3d_0.3-41 splines_3.4.3 lazyeval_0.2.0
[43] ModelMetrics_1.1.0 acepack_1.4.1 broom_0.4.3
[46] checkmate_1.8.5 yaml_2.2.0 reshape2_1.4.2
[49] abind_1.4-5 modelr_0.1.1 backports_1.1.2
[52] qvalue_2.10.0 Hmisc_4.1-1 caret_6.0-73
[55] tools_3.4.3 psych_1.7.8 gridBase_0.4-7
[58] gplots_3.0.1 proxy_0.4-22 ggridges_0.5.0
[61] Rcpp_0.12.18 plyr_1.8.4 base64enc_0.1-3
[64] rpart_4.1-12 pbapply_1.3-1 haven_1.1.2
[67] cluster_2.0.6 magrittr_1.5 data.table_1.11.6
[70] DO.db_2.9 openxlsx_4.1.0 reactome.db_1.62.0
[73] mvtnorm_1.0-5 hms_0.4.2 evaluate_0.11
[76] xtable_1.8-2 rio_0.5.10 mclust_5.2.2
[79] readxl_1.1.0 gridExtra_2.2.1 compiler_3.4.3
[82] KernSmooth_2.23-15 crayon_1.3.4 minqa_1.2.4
[85] R.oo_1.22.0 htmltools_0.3.6 segmented_0.5-1.4
[88] Formula_1.2-3 tclust_1.2-3 lubridate_1.7.1
[91] DBI_1.0.0 diffusionMap_1.1-0.1 MASS_7.3-48
[94] fpc_2.1-10 rappdirs_0.3.1 boot_1.3-20
[97] car_3.0-2 cli_1.0.0 R.methodsS3_1.7.1
[100] gdata_2.17.0 bindr_0.1 igraph_1.0.1
[103] pkgconfig_2.0.2 sn_1.5-0 rvcheck_0.1.0
[106] registry_0.3 numDeriv_2016.8-1 foreign_0.8-69
[109] xml2_1.1.1 foreach_1.4.3 rngtools_1.2.4
[112] pkgmaker_0.22 rvest_0.3.2 digest_0.6.12
[115] tsne_0.1-3 graph_1.56.0 rmarkdown_1.10
[118] cellranger_1.1.0 fastmatch_1.1-0 htmlTable_1.12
[121] curl_3.2 kernlab_0.9-25 gtools_3.5.0
[124] modeltools_0.2-22 graphite_1.24.1 nloptr_1.0.4
[127] nlme_3.1-131 jsonlite_1.5 carData_3.0-1
[130] fansi_0.3.0 viridisLite_0.3.0 pillar_1.3.0
[133] lattice_0.20-35 httr_1.3.1 DEoptimR_1.0-8
[136] survival_2.41-3 GO.db_3.5.0 glue_1.3.0
[139] zip_1.0.0 FNN_1.1 prabclus_2.2-6
[142] iterators_1.0.8 bit_1.1-14 class_7.3-14
[145] stringi_1.2.4 mixtools_1.0.4 blob_1.1.1
[148] latticeExtra_0.6-28 caTools_1.17.1 memoise_1.0.0
[151] irlba_2.1.2 ape_5.1